


"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					jnp.floataa
   __UpperCAmelCase							       =					True





   def 		UpperCamelCase_  (							self)  ->  str:
      super().setup()
      _lowerCamelCase	:					Optional[int]   =		nn.Dense(5      ,   dtype=self.dtype)





   def __call__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Optional[int]   =		super().__call__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		self.cls(outputs[2])
      return outputs[:2] + (cls_out,)







class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					FlaxBigBirdForNaturalQuestionsModule







def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      str						, __snake_case							:      List[str]						, __snake_case							:      Dict						, __snake_case							:      Tuple						, __snake_case							:      List[Any]   ):




   """simple docstring"""







   def cross_entropy(__snake_case							:      Optional[Any]						, __snake_case							:      str						, __snake_case							:      Optional[int]=None   ):
      _lowerCamelCase	:					Optional[int]   =		logits.shape[-1]
      _lowerCamelCase	:					Any   =		(labels[..., None] == jnp.arange(__snake_case   )[None]).astype("""f4"""   )
      _lowerCamelCase	:					Any   =		jax.nn.log_softmax(__snake_case						, axis=-1   )
      _lowerCamelCase	:					Any   =		-jnp.sum(labels * logits						, axis=-1   )
      if reduction is not None:
         _lowerCamelCase	:					int   =		reduction(__snake_case   )
      return loss

   _lowerCamelCase	:					int   =		partial(__snake_case						, reduction=jnp.mean   )
   _lowerCamelCase	:					int   =		cross_entropy(__snake_case						, __snake_case   )
   _lowerCamelCase	:					int   =		cross_entropy(__snake_case						, __snake_case   )
   _lowerCamelCase	:					List[str]   =		cross_entropy(__snake_case						, __snake_case   )
   return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class 	lowercase__				:
   __UpperCAmelCase							       =					"google/bigbird-roberta-base"
   __UpperCAmelCase							       =					3000
   __UpperCAmelCase							       =					10500

   __UpperCAmelCase							       =					128
   __UpperCAmelCase							       =					3

   __UpperCAmelCase							       =					1
   __UpperCAmelCase							       =					5

   # tx_args
   __UpperCAmelCase							       =					3e-5
   __UpperCAmelCase							       =					0.0
   __UpperCAmelCase							       =					20000
   __UpperCAmelCase							       =					0.0_0_9_5

   __UpperCAmelCase							       =					"bigbird-roberta-natural-questions"
   __UpperCAmelCase							       =					"training-expt"
   __UpperCAmelCase							       =					"data/nq-training.jsonl"
   __UpperCAmelCase							       =					"data/nq-validation.jsonl"





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      os.makedirs(self.base_dir      ,   exist_ok=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		os.path.join(self.base_dir      ,   self.save_dir)
      _lowerCamelCase	:					Tuple   =		self.batch_size_per_device * jax.device_count()







@dataclass
class 	lowercase__				:
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					4096  # no dynamic padding on TPUs





   def __call__(							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					Dict   =		self.collate_fn(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		jax.tree_util.tree_map(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
      return batch





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		self.fetch_inputs(features["""input_ids"""])
      _lowerCamelCase	:					Dict   =		{
          """input_ids""": jnp.array(SCREAMING_SNAKE_CASE      ,   dtype=jnp.intaa),
          """attention_mask""": jnp.array(SCREAMING_SNAKE_CASE      ,   dtype=jnp.intaa),
          """start_labels""": jnp.array(features["""start_token"""]      ,   dtype=jnp.intaa),
          """end_labels""": jnp.array(features["""end_token"""]      ,   dtype=jnp.intaa),
          """pooled_labels""": jnp.array(features["""category"""]      ,   dtype=jnp.intaa),
      }
      return batch





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					List[str]   =		[self._fetch_inputs(SCREAMING_SNAKE_CASE) for ids in input_ids]
      return zip(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      _lowerCamelCase	:					List[Any]   =		[1 for _ in range(len(SCREAMING_SNAKE_CASE))]
      while len(SCREAMING_SNAKE_CASE) < self.max_length:
         input_ids.append(self.pad_id)
         attention_mask.append(0)
      return input_ids, attention_mask







def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      Optional[Any]						, __snake_case							:      Union[str, Any]=None   ):




   """simple docstring"""







   if seed is not None:
      _lowerCamelCase	:					Optional[Any]   =		dataset.shuffle(seed=__snake_case   )
   for i in range(len(__snake_case   ) // batch_size   ):
      _lowerCamelCase	:					int   =		dataset[i * batch_size : (i + 1) * batch_size]
      yield dict(__snake_case   )

@partial(jax.pmap						, axis_name="""batch"""   )
def 				_snake_case      (   __snake_case							:      Any						, __snake_case							:      List[str]						, **__snake_case							:      Tuple   ):




   """simple docstring"""







   def loss_fn(__snake_case							:      str   ):
      _lowerCamelCase	:					List[Any]   =		model_inputs.pop("""start_labels"""   )
      _lowerCamelCase	:					Optional[Any]   =		model_inputs.pop("""end_labels"""   )
      _lowerCamelCase	:					List[str]   =		model_inputs.pop("""pooled_labels"""   )

      _lowerCamelCase	:					Optional[Any]   =		state.apply_fn(**__snake_case						, params=__snake_case						, dropout_rng=__snake_case						, train=__snake_case   )
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		outputs

      return state.loss_fn(
          __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, )

   _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		jax.random.split(__snake_case   )
   _lowerCamelCase	:					Any   =		jax.value_and_grad(__snake_case   )
   _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		grad_fn(state.params   )
   _lowerCamelCase	:					List[str]   =		jax.lax.pmean({"""loss""": loss}						, axis_name="""batch"""   )
   _lowerCamelCase	:					Tuple   =		jax.lax.pmean(__snake_case						, """batch"""   )

   _lowerCamelCase	:					int   =		state.apply_gradients(grads=__snake_case   )
   return state, metrics, new_drp_rng

@partial(jax.pmap						, axis_name="""batch"""   )
def 				_snake_case      (   __snake_case							:      Any						, **__snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		model_inputs.pop("""start_labels"""   )
   _lowerCamelCase	:					Any   =		model_inputs.pop("""end_labels"""   )
   _lowerCamelCase	:					Any   =		model_inputs.pop("""pooled_labels"""   )

   _lowerCamelCase	:					Optional[Any]   =		state.apply_fn(**__snake_case						, params=state.params						, train=__snake_case   )
   _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		outputs

   _lowerCamelCase	:					Tuple   =		state.loss_fn(__snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case						, __snake_case   )
   _lowerCamelCase	:					str   =		jax.lax.pmean({"""loss""": loss}						, axis_name="""batch"""   )
   return metrics




class 	lowercase__				(					train_state.TrainState				):
   __UpperCAmelCase							       =					struct.field(pytree_node=A_				)







@dataclass
class 	lowercase__				:
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					None





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=None)  ->  Optional[int]:
      _lowerCamelCase	:					Tuple   =		model.params
      _lowerCamelCase	:					Any   =		TrainState.create(
          apply_fn=model.__call__      ,   params=SCREAMING_SNAKE_CASE      ,   tx=SCREAMING_SNAKE_CASE      ,   loss_fn=SCREAMING_SNAKE_CASE      ,   )
      if ckpt_dir is not None:
         _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		restore_checkpoint(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Optional[Any]   =		{
             """lr""": args.lr,
             """init_lr""": args.init_lr,
             """warmup_steps""": args.warmup_steps,
             """num_train_steps""": num_train_steps,
             """weight_decay""": args.weight_decay,
         }
         _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		build_tx(**SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Union[str, Any]   =		train_state.TrainState(
             step=SCREAMING_SNAKE_CASE      ,   apply_fn=model.__call__      ,   params=SCREAMING_SNAKE_CASE      ,   tx=SCREAMING_SNAKE_CASE      ,   opt_state=SCREAMING_SNAKE_CASE      ,   )
         _lowerCamelCase	:					List[Any]   =		args
         _lowerCamelCase	:					Optional[Any]   =		data_collator
         _lowerCamelCase	:					Optional[Any]   =		lr
         _lowerCamelCase	:					Optional[Any]   =		params
      _lowerCamelCase	:					Optional[Any]   =		jax_utils.replicate(SCREAMING_SNAKE_CASE)
      return state





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      _lowerCamelCase	:					Dict   =		self.args
      _lowerCamelCase	:					List[str]   =		len(SCREAMING_SNAKE_CASE) // args.batch_size

      _lowerCamelCase	:					Optional[Any]   =		jax.random.PRNGKey(0)
      _lowerCamelCase	:					List[Any]   =		jax.random.split(SCREAMING_SNAKE_CASE      ,   jax.device_count())
      for epoch in range(args.max_epochs):
         _lowerCamelCase	:					Tuple   =		jnp.array(0      ,   dtype=jnp.floataa)
         _lowerCamelCase	:					Tuple   =		get_batched_dataset(SCREAMING_SNAKE_CASE      ,   args.batch_size      ,   seed=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Dict   =		0
         for batch in tqdm(SCREAMING_SNAKE_CASE      ,   total=SCREAMING_SNAKE_CASE      ,   desc=F'Running EPOCH-{epoch}'):
            _lowerCamelCase	:					Union[str, Any]   =		self.data_collator(SCREAMING_SNAKE_CASE)
            _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		self.train_step_fn(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
            running_loss += jax_utils.unreplicate(metrics["""loss"""])
            i += 1
            if i % args.logging_steps == 0:
               _lowerCamelCase	:					Optional[int]   =		jax_utils.unreplicate(state.step)
               _lowerCamelCase	:					int   =		running_loss.item() / i
               _lowerCamelCase	:					Tuple   =		self.scheduler_fn(state_step - 1)

               _lowerCamelCase	:					Optional[Any]   =		self.evaluate(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
               _lowerCamelCase	:					Optional[int]   =		{
                   """step""": state_step.item(),
                   """eval_loss""": eval_loss.item(),
                   """tr_loss""": tr_loss,
                   """lr""": lr.item(),
               }
               tqdm.write(str(SCREAMING_SNAKE_CASE))
               self.logger.log(SCREAMING_SNAKE_CASE      ,   commit=SCREAMING_SNAKE_CASE)

            if i % args.save_steps == 0:
               self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}'      ,   state=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					Optional[int]   =		get_batched_dataset(SCREAMING_SNAKE_CASE      ,   self.args.batch_size)
      _lowerCamelCase	:					Dict   =		len(SCREAMING_SNAKE_CASE) // self.args.batch_size
      _lowerCamelCase	:					str   =		jnp.array(0      ,   dtype=jnp.floataa)
      _lowerCamelCase	:					int   =		0
      for batch in tqdm(SCREAMING_SNAKE_CASE      ,   total=SCREAMING_SNAKE_CASE      ,   desc="""Evaluating ... """):
         _lowerCamelCase	:					int   =		self.data_collator(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Dict   =		self.val_step_fn(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
         running_loss += jax_utils.unreplicate(metrics["""loss"""])
         i += 1
      return running_loss / i





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Tuple   =		jax_utils.unreplicate(SCREAMING_SNAKE_CASE)
      print(F'SAVING CHECKPOINT IN {save_dir}'      ,   end=""" ... """)
      self.model_save_fn(SCREAMING_SNAKE_CASE      ,   params=state.params)
      with open(os.path.join(SCREAMING_SNAKE_CASE      ,   """opt_state.msgpack""")      ,   """wb""") as f:
         f.write(to_bytes(state.opt_state))
      joblib.dump(self.args      ,   os.path.join(SCREAMING_SNAKE_CASE      ,   """args.joblib"""))
      joblib.dump(self.data_collator      ,   os.path.join(SCREAMING_SNAKE_CASE      ,   """data_collator.joblib"""))
      with open(os.path.join(SCREAMING_SNAKE_CASE      ,   """training_state.json""")      ,   """w""") as f:
         json.dump({"""step""": state.step.item()}      ,   SCREAMING_SNAKE_CASE)
      print("""DONE""")







def 				_snake_case      (   __snake_case							:      Any						, __snake_case							:      Any   ):




   """simple docstring"""







   print(F'RESTORING CHECKPOINT FROM {save_dir}'						, end=""" ... """   )
   with open(os.path.join(__snake_case						, """flax_model.msgpack"""   )						, """rb"""   ) as f:
      _lowerCamelCase	:					Dict   =		from_bytes(state.params						, f.read()   )

   with open(os.path.join(__snake_case						, """opt_state.msgpack"""   )						, """rb"""   ) as f:
      _lowerCamelCase	:					str   =		from_bytes(state.opt_state						, f.read()   )

   _lowerCamelCase	:					Optional[Any]   =		joblib.load(os.path.join(__snake_case						, """args.joblib"""   )   )
   _lowerCamelCase	:					Optional[int]   =		joblib.load(os.path.join(__snake_case						, """data_collator.joblib"""   )   )

   with open(os.path.join(__snake_case						, """training_state.json"""   )						, """r"""   ) as f:
      _lowerCamelCase	:					str   =		json.load(__snake_case   )
   _lowerCamelCase	:					Tuple   =		training_state["""step"""]

   print("""DONE"""   )
   return params, opt_state, step, args, data_collator

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      Dict						, __snake_case							:      List[str]						, __snake_case							:      Any   ):




   """simple docstring"""







   _lowerCamelCase	:					List[Any]   =		num_train_steps - warmup_steps
   _lowerCamelCase	:					Optional[int]   =		optax.linear_schedule(init_value=__snake_case						, end_value=__snake_case						, transition_steps=__snake_case   )
   _lowerCamelCase	:					List[str]   =		optax.linear_schedule(init_value=__snake_case						, end_value=1E-7						, transition_steps=__snake_case   )
   _lowerCamelCase	:					Optional[Any]   =		optax.join_schedules(schedules=[warmup_fn, decay_fn]						, boundaries=[warmup_steps]   )
   return lr





def 				_snake_case      (   __snake_case							:      Optional[int]						, __snake_case							:      Tuple						, __snake_case							:      int						, __snake_case							:      Optional[Any]						, __snake_case							:      Dict   ):




   """simple docstring"""







   def weight_decay_mask(__snake_case							:      Union[str, Any]   ):
      _lowerCamelCase	:					Any   =		traverse_util.flatten_dict(__snake_case   )
      _lowerCamelCase	:					List[Any]   =		{k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
      return traverse_util.unflatten_dict(__snake_case   )

   _lowerCamelCase	:					List[str]   =		scheduler_fn(__snake_case						, __snake_case						, __snake_case						, __snake_case   )

   _lowerCamelCase	:					str   =		optax.adamw(learning_rate=__snake_case						, weight_decay=__snake_case						, mask=__snake_case   )
   return tx, lr



"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


